Authors:
Felipe Lodur
and
Wladmir Cardoso Brandão
Affiliation:
Pontifical Catholic University of Minas Gerais (PUC Minas), Brazil
Keyword(s):
Data Visualization, Social Media, Content Analysis, Stock.
Related
Ontology
Subjects/Areas/Topics:
Adaptive and Adaptable User Interfaces
;
Artificial Intelligence
;
Collaborative and Social Interaction
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Human-Computer Interaction
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Users interactions in social media have proven to be highly correlated with changes in the Stock Market, and the large volume of data generated every day in this market makes the manual analytical processing impractical. Data visualization tools are powerful to enable this analysis, generating insights to support decisions. In this article we present SSV, our data visualization approach to analyze social media stock-related content. In particular, we present the SSV architecture, as well as the techniques used by it to provide data visualization. Additionally, we show that the visualizations displayed by SSV are not disposed arbitrarily, by contrary, it uses a ranking system based on visualization entropy. Moreover, we perform experiments to evaluate the ranking system and the results show that SSV is effective to rank data visualizations. We also conducted a case study with finance specialists to capture the usefulness of our proposed approach, which points out room for improvements.